Mitigating measurement errors in multiqubit experiments

By Sergey Bravyi, S. Sheldon, Abhinav Kandala, David C. McKay, Jay M. Gambetta. 2020

324 citations recorded by the source corpus.

Reducing measurement errors in multi-qubit quantum devices is critical for performing any quantum algorithm. Here we show how to mitigate measurement errors by a classical post-processing of the measured outcomes. Our techniques apply to any experiment where measurement outcomes are used for computing expected values of observables. Two error mitigation schemes are presented based on tensor product and correlated Markovian noise models. Error rates parameterizing these noise models can be extracted from the measurement calibration data using a simple formula. Error mitigation is achieved by applying the inverse noise matrix to a probability vector that represents the outcomes of a noisy measurement. The error mitigation overhead, including the the number of measurements and the cost of the classical post-processing, is exponential in $\epsilon n$, where $\epsilon$ is the maximum error rate and $n$ is the number of qubits. We report experimental demonstration of our error mitigation methods on IBM Quantum devices using stabilizer measurements for graph states with $n\le 12$ qubits and entangled 20-qubit states generated by low-depth random Clifford circuits.

Subfields: Artificial Intelligence, Atomic and Molecular Physics, and Optics

View Paper: https://www.semanticscholar.org/paper/62801ad28f85da42d3d5ef8f04613ecfc7bd6416

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In Artificial Intelligence

In Atomic and Molecular Physics, and Optics

Citations in the corpus

Variational quantum algorithms (2020, methodology): “[219] introduced a method to mitigate expectation values against correlated measurement errors, while [220] implemented an error mitigation technique to suppress the effects of photon loss for a Gaussian Boson sampling device.”
Probabilistic error cancellation with sparse Pauli–Lindblad models on noisy quantum processors (2022, background): “There are quite a few algorithms for dealing with this type of noise, see for instance [33, 35, 36] for diﬀerent algorithms and further references.”
Error mitigation with Clifford quantum-circuit data (2020, background): “surement noise as it can be mitigated by specialized techniques [5, 11].”
Quantum error mitigation (2022, methodology): “Bravyi et al. (2021) try to construct the assignment matrix using continuous-time Markov processes with the generators (transition rate matrices) { G i } being single-and two-qubit operators: The assignment matrix A is now determined by 2 N 2 positive coefficients { r i } that can be learned by…”